Checkbox is a Series A technology company building AI-native SaaS for in-house legal teams.
Our platform helps legal teams manage how work is raised, understood, routed, actioned and resolved across the business.We are now transforming our SaaS ecosystem into agentic-first products, using AI agents and intelligent workflow automation to change how legal work gets done.
To support this, we are investing deeply in the data, context and retrieval foundations that power reliable AI product experiences.This is a critical next chapter for Checkbox.
Our data platform needs to become more than infrastructure.
It needs to become a competitive moat.The RoleWe are looking for a Principal Data Engineer / Data Architect to own Checkbox's data strategy and the architecture that delivers it.This is a senior technical leadership role reporting directly to the VP of Engineering.
You will lead our tier-one Data team, starting with one direct report, while remaining deeply hands‐on in the design and delivery of the systems you define.The Data team exists to serve the product engineering streams building AI agents, so close day‐to‐day partnership with Product, Engineering, AI and Platform teams is central to the role.You will own the data and AI reference architecture that underpins our agentic product direction.
This includes base data sources, intelligence services, context and retrieval layers, API and MCP surfaces, and the secure data access patterns required to give AI agents compliant, tenant‐isolated context.This is not a role for someone who only wants to design from a distance.
We need someone who has built data and context foundations for AI products in production, can make pragmatic architecture decisions, and can lead by building.What You'll OwnData and AI Reference ArchitectureOwn the data and AI reference architecture across Checkbox's product ecosystemDefine how base data sources, intelligence services, APIs, MCP surfaces, context engines and retrieval layers fit togetherDesign patterns that allow product engineering streams to build AI features on shared foundations rather than reinventing data access each timeEstablish clear architectural principles for storage, retrieval, serving, tenancy, observability and complianceWork hand in glove with the Principal Engineer on architecture that spans application, platform, eventing, data and AI systemsContext, Retrieval and AI Data FoundationsArchitect and build the context and retrieval layers that power AI agents and GenAI product experiencesDesign secure, tenant‐isolated data access patterns for AI systemsDefine how customer, matter, workflow, document, event and operational data should be modelled, retrieved and servedEvaluate and select the right data patterns for each problem, including transactional stores, operational data stores, warehouses, vector stores, semantic layers, knowledge graphs,



event streams and APIsMake buy‐vs‐build decisions layer by layer rather than defaulting everything to in‐house developmentEnsure AI agents receive the right context at the right time, with appropriate controls around access, relevance, latency, cost and complianceData Platform, Integrations and EventingOwn architecture across integrations, eventing, operational data, transactional data and AI‐ready dataDefine data contracts, event models, ingestion patterns and transformation approaches that scale across multiple productsPartner with product engineering teams to ensure recent product capabilities generate usable, reliable and well‐governed dataBuild shared data capabilities that support analytics, AI agents, workflow automation and customer‐facing product experiencesImprove data quality, lineage, observability and reliability across the data lifecycleSecurity, Tenancy and ComplianceTreat security, tenancy and data isolation as first‐order architectural concernsEnsure data access patterns are compliant, auditable and appropriate for enterprise customersDesign systems that support tenant isolation, data segregation, least privilege access and secure retrievalPartner with Platform, Security and Engineering teams on encryption, decryption, access control, auditability and production readinessEnsure data and context systems can support customer trust, compliance requirements and future audit needsTeam Leadership and Technical DirectionLead the Data team as a tier‐one engineering function reporting to the VP of EngineeringManage and grow one direct report initially, with scope to shape the team as the function expandsSet technical direction while staying close to implementationMentor engineers on data architecture, AI data foundations, retrieval patterns and pragmatic systems designBuild operating rhythms, standards and documentation that help the Data team scaleAct as the senior technical voice for data architecture across engineering leadership discussionsWhat Success Looks LikeProduct engineering streams can build AI features on a dependable, shared data and context layerThe data and AI reference architecture is real, documented, actively used and continuously improvedStorage, retrieval and serving choices genuinely fit the problem rather than forcing every use case through one patternData access is compliant, secure and tenant‐isolated by defaultAI agents can access relevant context reliably, with clear controls around quality, latency, cost and permissionsData contracts,



event models and retrieval patterns reduce duplication across product teamsThe Data team becomes a strategic enabler for AI product development rather than a bottleneckCheckbox's data and context capability becomes visibly stronger as a competitive moatAbout YouSignificant experience as a senior data engineer, principal data engineer, data architect, staff engineer or similar technical leadership roleProven experience building data and context foundations that power AI products in productionStrong experience designing data architectures across transactional, operational, analytical and AI‐ready systemsDeep understanding of modern data platform patterns, including data contracts, event‐driven architecture, ingestion, transformation, observability, lineage and governanceStrong understanding of AI data patterns such as retrieval systems, embeddings, semantic modelling, vector search, knowledge graphs, context engineering or agentic workflowsExperience working with multi‐tenant SaaS systems where data segregation, tenancy and access control matterAbility to make pragmatic architecture decisions and select the right tool or pattern for each problemStrong judgement around buy‐vs‐build decisions across data infrastructure, retrieval, orchestration and AI platform layersComfortable leading technical direction while still building, reviewing and shippingExperience mentoring engineers or leading small technical teamsStrong communication skills and ability to work closely with product engineering streams, platform teams and senior engineering leadershipComfortable operating in a fast‐moving environment where systems are being built, scaled and refined at the same timeBonus PointsExperience working on AI agents, agentic workflows, GenAI platforms or AI‐native SaaS productsExperience designing MCP or API surfaces for data and context accessExperience with legal tech, workflow automation, enterprise SaaS or document‐heavy productsExperience with AWS‐based data and platform infrastructureExperience with event‐driven systems, queues, Pub/Sub patterns or streaming architecturesExperience with data security, compliance, auditability and enterprise customer requirementsExperience growing a small data function from early foundations into a scalable teamWhat We OfferHybrid working with team days in our Sydney CBD officeDirect reporting line to the VP of EngineeringHigh ownership over a tier‐one engineering functionOpportunity to shape the data and AI architecture behind agentic‐first productsPersonal learning and development budgetFlexible leave policyExpense policy and salary sacrifice optionsCBD start‐up hub with snacks, drinks, premium coffee and team socialsCompany‐wide social events and annual off‐sitesTransparent, flat culture where questions and feedback are welcomed
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📌 Staff Data Engineer (New South Wales)
🏢 Checkbox
📍 New South Wales